Search Results (915)

Coalho cheese is a highly consumed fresh dairy product in northeastern Brazil; however, its high moisture content and susceptibility to microbial contamination limit shelf life and compromise product safety. In this context, active edible coatings have emerged as sustainable alternatives for food preservation. This study aimed to develop and characterize biodegradable edible coatings based on cassava starch, beeswax, and pomegranate peel extract and to evaluate their effectiveness in preserving coalho cheese during refrigerated storage. Films were characterized regarding physicochemical, optical, mechanical, barrier, and microstructural properties and subsequently applied as coatings to cheese samples stored at 7 ± 1 °C for 15 days. The incorporation of pomegranate peel extract and beeswax significantly affected film color, microstructure, and mechanical properties, while maintaining overall structural integrity. Formulations containing both additives exhibited improved flexibility and enhanced resistance to rupture, suggesting a combined effect of the lipid and phenolic components. Sensory evaluation performed 24 h after coating demonstrated high initial consumer acceptance, with overall acceptance scores above 6.7 on a nine-point hedonic scale and purchase intention comparable to the uncoated control. Microbiological analyses revealed that the coatings effectively delayed the growth of molds, yeasts, and aerobic mesophilic bacteria throughout storage. The formulation containing cassava starch, beeswax, and pomegranate peel extract (CSEB) showed the greatest preservation efficacy, reducing fungal populations by approximately 1.8 log cycles and maintaining significantly lower bacterial counts than the control after 15 days. In addition, coated cheeses exhibited lower titratable acidity and greater physicochemical stability. Overall, the results demonstrate that cassava starch-based active coatings incorporating beeswax and pomegranate peel extract represent a promising strategy for improving the microbiological stability and quality preservation of coalho cheese. Full article

Seafood is an important component of the Ecuadorian diet, particularly in coastal regions; however, contamination from anthropogenic activities has raised concerns about human exposure to toxic metals. This study quantified cadmium (Cd), cobalt (Co), lead (Pb), and total mercury (THg) in shrimp (Protrachypene precipua), shrimp-prawn (Litopenaeus vannamei), blue marlin (Makaira nigricans), and Pacific thread herring (PTH; Opisthonema sp.) purchased in Quito and Manta. A total of 71 samples were analyzed by atomic absorption spectrometry (flame and graphite furnace) and cold-vapor atomic fluorescence spectrometry. Distinct metal accumulation patterns were observed among species: blue marlin exhibited markedly elevated THg concentrations (1117.7 µg/kg) consistent with biomagnification processes, whereas shrimp-prawns showed the highest Cd levels (67.5 µg/kg). Pb and Co levels were below the corresponding limits of quantification in all samples. All detectable concentrations of Cd and THg remained below the maximum levels established by the Codex Alimentarius and European Union regulations. Risk characterization based on Hazard Quotient (HQ) indicated that THg intake from blue marlin exceeded the safety threshold (HQ > 1) across all demographic groups under both mean and maximum consumption scenarios. Additionally, HQ values above 1 were observed for toddlers and children consuming shrimp, shrimp-prawn, and PTH under the maximum exposure scenario. In contrast, Cd exposure remained below the level of concern for all seafood products and population groups. Overall, blue marlin was the primary contributor to potential mercury exposure, particularly among younger consumers. Full article

Artificial intelligence and image-based deep learning offer promising non-destructive approaches for rapid fish quality assessment. This study evaluated the potential of deep learning models to classify freshness-related sensory attributes and predict color parameters of European seabass (Dicentrarchus labrax) fillets using image data acquired under controlled illumination. Seven sensory quality parameters and six color attributes were assessed. A structured image dataset was obtained at a fixed camera-to-sample distance of 45 cm, and 65 independently trained models, comprising 35 classification and 30 regression models, were developed using DenseNet121, InceptionV3, MobileNet, VGG16, and VGG19 architectures. Model training and evaluation were performed using group-based 10-fold cross-validation to ensure that images from the same fillet were not shared between the training and test sets. Classification performance depended strongly on both architecture and target parameter. DenseNet121 achieved the highest accuracy for purchasability (92.60%), water release (90.20%), odor (75.60%), color (77.40%), and disintegration (87.70%), whereas MobileNet performed best for texture (81.20%) and total score (57.70%). Purchasability was the most predictable sensory parameter across architectures, with a mean accuracy of 86.84%, whereas total score was the least predictable at 45.64%. In the regression tasks, DenseNet121 achieved the lowest MAE for a* (0.680), L* (2.467), hue (0.961), and whiteness (2.357), while MobileNet performed best for b* (1.200) and chroma (1.024). Grad-CAM visualizations indicated that model activations were predominantly associated with the fillet region, although more difficult classes showed more fragmented and occasionally edge- or background-associated activation patterns. Overall, DenseNet121 provided the strongest and most consistent performance, ranking first for nine of the 13 prediction targets. These findings demonstrate the potential of RGB imaging and deep learning for rapid, objective, and non-destructive assessment of selected freshness and quality attributes of European seabass fillets. Full article

Floriculture and ornamental horticulture face pressure to improve environmental and social performance without undermining economic viability, yet evidence on how sustainability becomes economic value remains fragmented. This systematic mapping and bibliometric review examines the field’s evolution and structure, the conditions under which sustainability attributes generate consumer value, and the firm-level and governance factors shaping adoption and value distribution. Scopus and Web of Science were searched on 10 May 2026 for English-language journal articles published from 2015 onwards. A PRISMA-guided process retained 77 studies, analysed through descriptive statistics, VOSviewer co-occurrence mapping, thematic classification and narrative synthesis. Publication activity became more continuous after 2020, but consumer preferences and willingness to pay accounted for 32.5% of the corpus. Bibliometric mapping identified three connected domains: sectoral sustainability and competitiveness, consumer valuation, and business adoption. Local origin, certification, pollinator-friendly production, reduced pesticide use and native plants can support differentiation and price premiums when aesthetic and functional quality, acceptable prices, label comprehension and trust are maintained. Producer-side evidence is less developed: adoption depends on investment and labour requirements, production risk, finance and buyer coordination. Certification may improve reputation and market access, while compliance costs, scale requirements and bargaining asymmetries can exclude smaller firms or prevent benefits from reaching growers and workers. The review distinguishes market potential, firm-level viability and realised value-chain outcomes. Sustainability creates economic value when consumer recognition, production feasibility and credible governance are aligned; future research should connect observed purchases and transaction prices with adoption costs, contracts and measurable environmental and social outcomes. Full article

The growing demand for dairy-free functional foods has created a need for fruit-based products that simultaneously provide probiotics, prebiotics, and bioactive compounds. This study evaluated seven high-fruit synbiotic-designed sorbets prepared with raspberries, strawberries, haskap berries, apples, or plums, and two variants with blackcurrants, containing 45–55% fruit, 3% inulin, and Lactiplantibacillus plantarum 299v added at two inoculum levels. Sucrose was added to the mixture, and in one variant of the blackcurrant, it was replaced with erythritol, which can affect, among other things, the sweetness, hardness, melting point, mouthfeel, and freezing point of frozen desserts. Erythritol was used to evaluate the effect on these characteristics in one variant. The addition of one and two probiotic capsules to each sorbet was also compared. The viability of the probiotics (as LAB) before and after freezing, physicochemical composition, color, hardness, melting, aeration, antioxidant properties, consumer acceptance, and purchase intention were assessed using univariate and multivariate statistical analyses. After freezing, the number of viable cells remained at approximately 7.05–7.18 log CFU/g in the single-capsule variants and 7.45–7.51 log CFU/g in the two-capsule variants, confirming the high immediate viability of the probiotic cells. The fruit type and its content in the matrix significantly differentiated acidity, sugars, color, hardness, degree of growth, melting properties, phenolic compounds, anthocyanins, antioxidant activity, and sensory quality. The dark berry sorbets showed a particularly high content of bioactive compounds. Consumer responses varied significantly between formulations, with taste, texture, and consistency demonstrating the strongest associations with overall acceptance. The results demonstrate that fruit sorbets can serve as practical, dairy-free carriers for Lactiplantibacillus plantarum 299v and inulin, while retaining fruit-specific functional and sensory properties. Full article

Liquefied Petroleum Gas (LPG) subsidies have become an important policy instrument for improving energy affordability, reducing energy poverty, and supporting economic activities among vulnerable populations in Indonesia. However, the long-term sustainability of LPG subsidy policies remains a subject of debate due to concerns regarding fiscal burdens, governance challenges, and environmental impacts. This study aims to assess the sustainability of LPG subsidy policies through a case study in Cirebon City, West Java, Indonesia, using the Triple Bottom Line framework that integrates social, economic, and environmental dimensions. A qualitative case study approach was employed, utilizing semi-structured interviews, Focus Group Discussions (FGDs), field observations, and document analysis involving key stakeholders, including government agencies, Pertamina, LPG distributors, micro-enterprises, and household beneficiaries. Data were analyzed using thematic analysis. The findings indicate that LPG subsidies contribute positively to social sustainability by improving energy access, reducing household energy expenditures, and enhancing social welfare. The policy also supports economic sustainability through increased purchasing power, improved productivity of micro, small, and medium enterprises (MSMEs), and local economic resilience. From an environmental perspective, LPG subsidies help reduce dependence on traditional fuels such as firewood and kerosene, thereby contributing to cleaner household energy use and indirectly supporting forest conservation. The study identifies significant challenges related to mistargeting, distribution leakages, fiscal pressures, and continued dependence on fossil fuels, which may hinder long-term sustainability and energy transition efforts. The findings suggest that LPG should be positioned as a transition fuel rather than a permanent energy solution. Strengthening subsidy-targeting mechanisms, improving digital governance systems, enhancing fiscal efficiency, and gradually promoting renewable energy adoption are essential to support sustainable energy governance and the achievement of Sustainable Development Goals (SDGs). This study contributes to the literature by providing an integrated sustainability assessment of LPG subsidy policies within the context of developing countries. Full article

This study examines ethical transparency, trust in influencers, attitudes toward influencer-promoted products, purchase intention, and self-reported purchasing behavior within Instagram-based influencer marketing. The research is positioned within the broader context of sustainable digital consumer behavior, emphasizing the importance of transparent and responsible communication in contemporary digital marketing ecosystems and sponsored influencer content. The study draws on the Persuasion Knowledge Model (PKM) and the Theory of Planned Behavior (TPB) to examine consumer awareness of influencers’ financial motives alongside perceptions, purchase intentions, and self-reported purchasing behavior. The research employed a quantitative design based on a structured online questionnaire among 491 respondents from various countries, who were professionally active young adults enrolled in business and management programs in France. The findings indicate that the respondents demonstrate high awareness of the commercial nature of influencer marketing and strong expectations regarding sponsorship disclosure. At the same time, trust in influencers remained moderate, while attitudes toward influencer-promoted products and purchase intentions were only moderately positive. Self-reported purchasing behavior was also relatively limited, while personal need received the highest mean score among the examined purchasing motivations. The results indicate that respondents in this study demonstrated high awareness of commercial persuasion and comparatively cautious responses to sponsored influencer content. The study contributes to sustainability-oriented marketing research by considering the relevance of ethical transparency, consumer awareness, and responsible communication to informed and responsible digital consumption. Sustainable consumption was not directly measured; rather, the study discusses the sustainability relevance of the observed consumer response patterns. Full article

The rapid decarbonisation of urban districts is accelerating the deployment of rooftop photovoltaic (PV) systems, household battery energy storage systems (BESSs) and electric vehicles (EVs). However, the high variability of urban micro-generation creates substantial forecasting errors at the urban–grid interface, imposing high balancing costs on distribution system operators (DSOs) and local energy communities. Traditional metrics fail to assess how effectively internal peer-to-peer (P2P) flexibility offsets these forecast mismatches prior to grid settlement. To address this gap, this paper presents a novel methodological framework and a non-parametric indicator: the Community Imbalance Neutralisation Index (CINI). Formulated in a generalised, scalable matrix structure applicable to any heterogeneous urban neighbourhood (N households), CINI quantifies the relative reduction in net community-level imbalance relative to the cumulative sum of uncoordinated individual forecast errors. CINI ranges from 0 per cent (no collective mitigation) to 100 per cent (perfect internal neutralisation). Complementing this index, an adaptive day-ahead scheduling algorithm is introduced to determine the optimal community energy purchase requirement (E_forecast). The proposed framework is numerically evaluated using a high-resolution synthetic benchmark annual dataset with 15 min intervals (35,040 intervals) representing a Central European urban residential cluster equipped with diverse combinations of PV, BESS and managed EV charging infrastructure. The simulation results demonstrate that active cVPP coordination reduces annual grid-facing imbalance energy from 188.73 MWh to 143.88 MWh, increasing the annual CINI score from 57.22% to 67.39% (+10.17 percentage points) compared with the uncoordinated baseline. Notably, the framework reveals a ‘Winter Flexibility Paradox’, achieving its highest relative efficacy during the winter months (+13.88 percentage points in December). Furthermore, sensitivity analyses show that scaling flexibility up to 40 kW achieves a CINI score of 91.22%, revealing diminishing marginal returns and critical technological saturation thresholds. The proposed CINI metric and E_forecast dispatch algorithm provide city planners, municipal energy managers and DSOs with a transparent diagnostic tool to design dynamic socio-economic tariff incentives, optimise urban micro-grid sizing, prevent free-rider dynamics, and foster resilient, self-balancing smart cities. Full article

The growing penetration of renewable energy sources introduces significant uncertainties into integrated energy systems (IESs). Conventional single-timescale management strategies, typically designed for static power balance, fail to address the symmetry of source-load uncertainties arising from both supply and demand sides. To address this challenge, this paper proposes a multi-timescale optimal scheduling framework that integrates demand response (DR) and multi-energy flow coupling. The framework adopts a hierarchical progressive strategy across day-ahead, intra-day, and real-time stages. The day-ahead stage optimizes the economic baseline with an hourly resolution. The intra-day stage conducts rolling correction at 15 min intervals to activate slow-response equipment flexibility, boosting combined heat and power (CHP) generation by 40.70% and increasing waste-heat cooling consumption by 41.12%. The real-time stage employs energy storage at 5 min resolution to suppress fluctuations, maintaining electricity, heat, and cooling load deviations, respectively, at remarkably low levels of 0.17%, 0.10%, and 0.06%. Comparative results show that with power-to-gas (P2G) integration, the system purchases off-peak electricity for synthetic natural gas production, cutting gas procurement costs by 12.70% and reducing net carbon emissions from 5.14 t to 4.91 t. DR mechanisms enable a gas–electricity substitution strategy that lowers electricity purchase costs by 9.97%, reduces evening peak electric vehicle (EV) charging load by 8.32%, and decreases charging expenses by 15%. Full article

This study examines recurrent pet care spending, its perceived economic incidence on household budgets, and barriers to e-commerce adoption in Yopal, Colombia. Using a quantitative, cross-sectional, non-experimental design, data were collected from a non-probability convenience sample of 619 pet owners. Descriptive statistics, chi-square and Fisher’s exact tests, and binary logistic regression models were applied; online-channel adoption was analyzed exploratorily. Higher pet food and annual healthcare expenditures and ownership of two pets were associated with greater odds of perceiving moderate-to-high economic incidence, while partial affective justification was also associated with greater perceived economic incidence. Online purchasing remained marginal, with only 31 respondents identifying it as their primary purchasing channel. Among non-adopters, distrust of digital platforms and limited internet access were the most frequent barriers, with distributions differing significantly across age and income groups. Overall, the findings are consistent with an economically active but digitally incomplete market configuration, where recurrent demand coexists with limited digital transactional development. Full article

The implementation of global net-zero emission policies and carbon pricing mechanisms, including the Carbon Border Adjustment Mechanism (CBAM), has increased the need for export-oriented manufacturers to make carbon-related costs visible in production decisions. Taiwan’s fastener industry is particularly exposed because of its multi-stage production structure, intensive use of steel-based inputs, and reliance on international markets. Traditional single-base cost allocation can obscure differences in resource consumption across products and processes. To enhance the decision-making value of cost information, this study develops an Activity-Based Costing (ABC) framework that assigns modeled resource consumption to activities and products while separately internalizing carbon-related costs in a mixed-integer production-planning model. Theory of Constraints (TOC) is used as a bottleneck-focused managerial interpretation of finite production capacities rather than as a separate objective function. Using Taiwan’s fastener manufacturing industry as the research context, the model is demonstrated through an illustrative numerical example parameterized to reflect representative production and resource consumption relationships. The reported findings are therefore numerical results of the model rather than statistical empirical validation of a sampled population or a disclosed company dataset. Under the specified baseline parameters, Product 4 is selected because its revenue, purchasing-tier, activity driver, capacity, and carbon-related cost coefficients jointly dominate the alternatives. Product exclusion scenarios further show how the preferred product changes when individual products are exogenously removed while carbon price parameters are held fixed. The framework provides a transparent decision support structure for jointly examining activity costs, constrained resources, and carbon-related charges; it should not be interpreted as a legal replication of CBAM certificate obligations. Industry 5.0 extensions are positioned as future research. Full article

Many people are affected by difficult-to-understand clinical phenomena associated with acute SARS-CoV-2 infection and by post-acute sequelae of COVID-19 (PASC, or long COVID, LC). The mechanisms responsible for the clinical phenomena have not been well established. The host cell receptor for SARS-CoV-2 is human angiotensin-converting enzyme 2 (ACE2), which binds the SARS-CoV-2 spike protein receptor-binding domain (RBD) to initiate infection. We hypothesized that some people may produce anti-RBD antibodies that sufficiently resemble ACE2 structure to have ACE2-like catalytic activity after infection, and such antibodies are hypothesized to contribute to disease pathogenesis. Our previous studies showed that ACE2-like catalytic activity was associated with immunoglobulin in some acute and convalescent COVID-19 patients. ACE2-like catalytic activity correlated with blood pressure changes following a moderate exercise challenge in people convalescing from COVID-19. To further establish that ACE2-like catalytic activity could be attributed to antibodies, we screened human monoclonal antibodies (mAbs) against SARS-CoV-2 spike protein from three different research centers and others purchased from a commercial source for ACE2-like catalytic activity. We identified four human monoclonal antibodies with ACE2-like catalytic activity. The ACE2-like catalytic activity of these mAbs was not inhibited by MLN-4760, a compound that inhibits native human ACE2 catalytic activity, nor by EDTA, unlike native ACE2, a zinc metalloprotease, but was inhibited by an overlapping pool of spike peptides. Enzyme kinetic studies showed that the mAbs had substantially lower Vmax and K_m values than native ACE2, consistent with the characteristics of other catalytic antibodies. The data therefore suggested that the antibodies cleave ACE2 substrate via a mechanism different from native ACE2. The identification of specific mAbs with ACE2-like catalytic activity supports the hypothesis that antibodies induced by SARS-CoV-2 infection could help mediate the pathogenesis of COVID-19 and LC, and, more generally, the hypothesis that catalytic antibodies induced by infectious agents can contribute to disease pathogenesis. Full article

Rising rates of obesity and diabetes have increased demand for sugar-free products, while consumers also seek natural colorants and foods offering functional health benefits. Sorghum leaves, an underutilized part of the plant, contain health-promoting polyphenolic compounds with bioactive and coloring properties. Incorporating sorghum leaf-derived polyphenols into foods may meet both producer and consumer interest in natural colorants and value-added ingredients. This study aimed to incorporate sorghum leaf polyphenol extracts into sugar-free gelatin candies. Four formulations were produced: artificial-low color, natural-low color, artificial-high color, and natural-high color candies. Instrumental texture was evaluated using firmness and springiness measurements. Water activity was recorded using a water activity meter. Color was assessed with a colorimeter. Total phenolic content (TPC) was measured using the Folin–Ciocalteu assay. Consumers’ (N = 105) acceptance of sensory properties was evaluated using 9-point hedonic scales, and purchase intent was measured before and after colorant source and sweetener informational messaging. Natural colorant formulations had significantly higher water activity than artificially colored candies. Instrumental firmness and springiness differed significantly (p < 0.05) across formulations. However, these differences did not significantly (p > 0.05) impact consumer texture preference. TPC increased with higher levels of sorghum leaf extract but did not increase bitterness ratings. After receiving educational information about the colorant sources, purchase intent for the naturally colored formulations significantly (p < 0.05) increased, exceeding that of artificially colored candies. These findings demonstrate that sorghum leaf polyphenols can be successfully incorporated into sugar-free gummy candies and are well accepted by consumers as a natural colorant. This work supports sorghum as a value-added food ingredient, creating new market opportunities for US sorghum farmers. Full article

Higher education institutions are increasingly expected to reduce greenhouse gas emissions while maintaining reliable educational, laboratory, and administrative operations. This challenge is particularly relevant in transition economies, where university campuses often depend on fossil-fuel-based electricity systems, natural-gas heating, and aging infrastructure. This study presents a campus-scale decarbonization assessment for the Tashkent Institute of Chemical Technology in Uzbekistan. The quantified inventory covered Scope 1 emissions from natural-gas combustion and Scope 2 emissions from purchased electricity. Paper use, digital services, behavioural measures, and campus greening were assessed as supplementary institutional indicators and were excluded from the quantified total because consistent pre- and post-intervention activity data were unavailable. The assessment combined institutional utility records for 2023–2025 with information on renewable-energy deployment, heating modernization, digital transformation, sustainability awareness, and campus greening. A 300 kW solar photovoltaic system comprising 666 modules was commissioned in May 2023, with a documented annualized generation potential of approximately 520,000 kWh. Purchased grid electricity amounted to 711,402, 745,947, and 749,060 kWh in 2023, 2024, and 2025, respectively, while annual natural-gas consumption was 144,775, 161,200, and 142,031 m³. Using a conservative standard-based net calorific value of 31.8 MJ/m³ together with IPCC stationary-combustion factors, annual Scope 1 and Scope 2 emissions were estimated at 637.53, 685.29, and 652.65 tCO₂-eq, respectively. The 2025 total was 4.76% below the 2024 value but 2.37% above the 2023 value. The annualized PV technical potential corresponds to a theoretical maximum Scope 2 displacement of 276.64 tCO₂-eq/year under 100% self-consumption. This value does not represent measured generation or a realized emission reduction and was not included in the quantified inventory. Digitalization, behavioural engagement, and greening were evaluated as complementary measures rather than assigned separate emission-reduction credits. The study provides a transparent and regionally relevant framework for universities in transition economies seeking to strengthen campus carbon management under incomplete data conditions. Full article

Generative AI shopping assistants are becoming a primary touchpoint for online consumers, yet they often produce convincing but inaccurate content, a phenomenon known as AI hallucination that poses an under-studied risk to consumer trust in e-commerce. This study explains that phenomenon by building and testing a moderated mediation model grounded in Expectation Violation Theory, Epistemic Vigilance Theory, and Algorithmic Trust Repair Theory. A sequential, exploratory mixed-methods design was used: a qualitative phase identified the dimensions and configurational pathways of consumer trust withdrawal using the Gioia methodology and fuzzy-set Qualitative Comparative Analysis, and a subsequent large-scale quantitative phase tested and refined the resulting model across a multi-country European sample using partial least squares structural equation modeling and Necessary Condition Analysis. The results show that exposure to hallucinations triggers expectation violation, activating epistemic vigilance and reducing perceived AI competence; this sequence drives trust recalibration, reflected in lower continued-use and purchase intentions and greater negative word-of-mouth. AI literacy, prior trust, and transparency cues significantly moderate these relationships, and structural trust repair mechanisms, namely retrieval-augmented generation and uncertainty disclosure, prove more effective than purely communicative repair strategies. Theoretically, this study advances a dynamic account of trust recalibration in AI-mediated commerce; practically, it offers concrete guidance for platform design and regulatory policy under the EU AI Act. Full article